Unless someone has a good argument for concordance, I'd go with just
pearson for the moment.
Feel free to provide some examples / use cases where concordance is more
helpful, so we can decide to include it, too.
Given many options without guidance is not that great usually.
On 09/08/2015 01:47 PM, Stylianos Kampakis wrote:
I have already made an implementation of the concordance correlation
coefficient. I can also make one for Pearson, write up some examples
that shows the difference between them and then make a pull request.
2015-09-08 16:15 GMT+01:00 Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>>:
I'm also +1 on Pearson, and unsure about concordance ;)
On 09/08/2015 10:43 AM, Mathieu Blondel wrote:
Maybe I misunderstood Alex's comment but I thought he meant
(Pearson) correlation.
Alex, when you mentioned brain imaging, did you mean Pearson
correlation or concordance correlation?
I can't comment on concordance correlation as I was not familiar
with it.
On Tue, Sep 8, 2015 at 3:12 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
On 09/08/2015 06:42 AM, Mathieu Blondel wrote:
Pearson correlation between y_true and y_pred is also a
standard evaluation metric in genomic selection. In a
sense, it can be seen as a ranking measure since y_true
and y_pred don't need to be equal: they only need to be
collinear to achieve perfect correlation.
+1 for adding pearson_correlation_score
I thought we were discussiong the concordance correlation
coefficient?
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